雾计算环境下基于边缘亲和力的应用管理

Md. Redowan Mahmud, K. Ramamohanarao, R. Buyya
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引用次数: 28

摘要

雾计算通过将计算设施扩展到更靠近数据源的地方,克服了在远程云数据中心执行物联网(IoT)应用程序的限制。由于大多数雾节点都是资源受限的,因此在雾环境中适应每个物联网应用是非常具有挑战性的。因此,我们需要有效地确定应该在Fog中部署哪一组应用程序。如果同时考虑应用程序在紧急性、规模和输入流量方面的特征,则变得更加复杂。时间优化执行的必要性进一步加剧了应用程序管理问题。在这项工作中,我们为雾环境提出了一种策略,该策略跨网关和基础设施级别分发应用程序管理任务。它根据应用程序的边缘亲和力对其进行分类和放置。应用程序的边缘亲和性表示与其特征相一致的不同属性的相对强度,例如用户定义的截止日期、每次输入的数据量和物联网设备的感知频率,这些都需要在雾环境中解决,以满足其服务质量(QoS)。建议的策略还可以最大限度地减少Fog基础设施中应用程序的服务交付时间。在ifogsim模拟环境和基于fogbus的真实环境中,将其性能与现有的应用管理策略进行了比较。实验结果表明,该策略在QoS增强、网络松弛和资源利用率方面优于其他策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge Affinity-based Management of Applications in Fog Computing Environments
Fog computing overcomes the limitations of executing Internet of Things (IoT) applications in remote Cloud datacentres by extending the computation facilities closer to data sources. Since most of the Fog nodes are resource constrained, accommodation of every IoT application within Fog environments is very challenging. Hence, we need to efficiently identify which set of applications should be deployed in Fog. It becomes even more complicated when the application characteristics in terms of urgency, size and flow of inputs are considered simultaneously. The necessity of time-optimized execution further intensifies the application management problem. In this work, we propose a policy for Fog environments that distributes application management tasks across the gateway and the infrastructure level. It classifies and places applications according to their Edge affinity. Edge affinity of an application denotes the relative intensity of different attributes coherent with its characteristics such as user-defined deadline, amount of data per input and sensing frequency of IoT devices, which are required to be addressed within Fog environments to meet its Quality of Service (QoS). The proposed policy also minimizes the service delivery time of applications in Fog infrastructure. Its performance is compared with existing application management policies in both iFogSim-simulated and FogBus-based real environments. The experiment results show that our policy outperforms others in combined QoS enhancement, network relaxation and resource utilization.
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